Summary of Representative Arm Identification: a Fixed Confidence Approach to Identify Cluster Representatives, by Sarvesh Gharat et al.
Representative Arm Identification: A fixed confidence approach to identify cluster representatives
by Sarvesh Gharat, Aniket Yadav, Nikhil Karamchandani, Jayakrishnan Nair
First submitted to arxiv on: 26 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Probability (math.PR); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper studies the Representative Arm Identification (RAI) problem in the Multi-Armed Bandits (MAB) framework. The goal is to reliably identify a certain number of arms from each cluster while using as few arm pulls as possible. The RAI problem covers several well-studied MAB problems, including identifying the best arm or any out of the top K. The paper provides instance-dependent lower and upper bounds on the sample complexity of feasible algorithms for this setting. Two confidence interval-based algorithms are proposed, which orderwise match the lower bound. An empirical comparison with an LUCB-type alternative is conducted on synthetic and real-world datasets, showing the superior performance of the proposed schemes in most cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to identify important items from a group without wasting too many tries. It’s like trying to find the best flavor of ice cream by tasting just a few samples. The researchers created special methods to do this and tested them on real-world data, finding that their approaches worked well in most cases. |
